{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sales Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"\\ndf_1=pd.read_csv(\\'Sales_April_2019.csv\\')\\ndf_all=pd.DataFrame()\\nfiles=[file for file in os.listdir() if (file.startswith(\\'Sale\\'))]\\nfor f in files:\\n    df=pd.read_csv(f)\\n    df_all=pd.concat([df_all,df])\\n\\n#df_all.to_csv(\"sales_Data.csv\",index=False)\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate 12 months data into a single csv:\n",
    "\"\"\"\"\n",
    "df_1=pd.read_csv('Sales_April_2019.csv')\n",
    "df_all=pd.DataFrame()\n",
    "files=[file for file in os.listdir() if (file.startswith('Sale'))]\n",
    "for f in files:\n",
    "    df=pd.read_csv(f)\n",
    "    df_all=pd.concat([df_all,df])\n",
    "\n",
    "#df_all.to_csv(\"sales_Data.csv\",index=False)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186850 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0        176558        USB-C Charging Cable                2      11.95   \n",
       "1           NaN                         NaN              NaN        NaN   \n",
       "2        176559  Bose SoundSport Headphones                1      99.99   \n",
       "3        176560                Google Phone                1        600   \n",
       "4        176560            Wired Headphones                1      11.99   \n",
       "...         ...                         ...              ...        ...   \n",
       "186845   259353      AAA Batteries (4-pack)                3       2.99   \n",
       "186846   259354                      iPhone                1        700   \n",
       "186847   259355                      iPhone                1        700   \n",
       "186848   259356      34in Ultrawide Monitor                1     379.99   \n",
       "186849   259357        USB-C Charging Cable                1      11.95   \n",
       "\n",
       "            Order Date                         Purchase Address  \n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001  \n",
       "1                  NaN                                      NaN  \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215  \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  \n",
       "...                ...                                      ...  \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001  \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016  \n",
       "\n",
       "[186850 rows x 6 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all=pd.read_csv('sales_Data.csv',parse_dates=[\"Order Date\"])\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing values\n",
    "df_all.isna().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            186305\n",
       "Product             186305\n",
       "Quantity Ordered    186305\n",
       "Price Each          186305\n",
       "Order Date          186305\n",
       "Purchase Address    186305\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all[['Order ID','Product','Quantity Ordered','Price Each','Order Date','Purchase Address']].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x221a9ecb888>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_all.isnull(), yticklabels = False, cbar = False, cmap=\"Blues\")\n",
    "#ploting the missing values we notice they are in the same rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186305 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0        176558        USB-C Charging Cable                2      11.95   \n",
       "2        176559  Bose SoundSport Headphones                1      99.99   \n",
       "3        176560                Google Phone                1        600   \n",
       "4        176560            Wired Headphones                1      11.99   \n",
       "5        176561            Wired Headphones                1      11.99   \n",
       "...         ...                         ...              ...        ...   \n",
       "186845   259353      AAA Batteries (4-pack)                3       2.99   \n",
       "186846   259354                      iPhone                1        700   \n",
       "186847   259355                      iPhone                1        700   \n",
       "186848   259356      34in Ultrawide Monitor                1     379.99   \n",
       "186849   259357        USB-C Charging Cable                1      11.95   \n",
       "\n",
       "            Order Date                         Purchase Address  \n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001  \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215  \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001  \n",
       "...                ...                                      ...  \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001  \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016  \n",
       "\n",
       "[186305 rows x 6 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df_all.dropna(how='all')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().values.any()\n",
    "#no more missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            object\n",
       "Product             object\n",
       "Quantity Ordered    object\n",
       "Price Each          object\n",
       "Order Date          object\n",
       "Purchase Address    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n",
    "#converting step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(df['Quantity Ordered'].iloc[517],df['Quantity Ordered'].mode()[0],inplace=True)\n",
    "df['Quantity Ordered'].iloc[517]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quantity Ordered']=pd.to_numeric(df['Quantity Ordered'])\n",
    "df['Quantity Ordered'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'11.95'"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(df['Price Each'].iloc[517],df['Price Each'].mode()[0],inplace=True)\n",
    "df['Price Each'].iloc[517]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price Each']=pd.to_numeric(df['Price Each'])\n",
    "df['Price Each'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186305 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  \n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90  \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99  \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00  \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99  \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99  \n",
       "...                ...                                      ...     ...  \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97  \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00  \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00  \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99  \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95  \n",
       "\n",
       "[186305 rows x 7 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adding the sales column \n",
    "df['Sales']=df['Price Each']*df['Quantity Ordered']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Month</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  Month  \n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90      4  \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99      4  \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00      4  \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99      4  \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99      4  \n",
       "...                ...                                      ...     ...    ...  \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97      9  \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00      9  \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00      9  \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99      9  \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95      9  \n",
       "\n",
       "[185950 rows x 8 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Order Date'].str[0:2]!='Or']\n",
    "df['Month'] = df['Order Date'].str[0:2]\n",
    "df['Month']=pd.to_numeric(df['Month'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.613443e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.736727e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.390670e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.199603e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.152607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.807100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.647776e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.577802e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.244468e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.202022e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.097560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.822257e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales\n",
       "Month              \n",
       "12     4.613443e+06\n",
       "10     3.736727e+06\n",
       "4      3.390670e+06\n",
       "11     3.199603e+06\n",
       "5      3.152607e+06\n",
       "3      2.807100e+06\n",
       "7      2.647776e+06\n",
       "6      2.577802e+06\n",
       "8      2.244468e+06\n",
       "2      2.202022e+06\n",
       "9      2.097560e+06\n",
       "1      1.822257e+06"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What was the best month for sales? How much was earned that month?\n",
    "df.groupby('Month').sum()['Sales'].sort_values(ascending=False).to_frame()\n",
    "#we notice it's month 12 & 10 & 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sales per month')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis = df.groupby('Month').sum()[['Sales']].plot(figsize = (10,5), marker = '*', color = 'g')\n",
    "plt.xticks(range(13))\n",
    "plt.grid()\n",
    "axis.set_title('Sales per month')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Month</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Boston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  \\\n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90   \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99   \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00   \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99   \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99   \n",
       "...                ...                                      ...     ...   \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97   \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00   \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00   \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99   \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95   \n",
       "\n",
       "        Month           city  \n",
       "0           4         Dallas  \n",
       "2           4         Boston  \n",
       "3           4    Los Angeles  \n",
       "4           4    Los Angeles  \n",
       "5           4    Los Angeles  \n",
       "...       ...            ...  \n",
       "186845      9    Los Angeles  \n",
       "186846      9  San Francisco  \n",
       "186847      9  San Francisco  \n",
       "186848      9  San Francisco  \n",
       "186849      9  San Francisco  \n",
       "\n",
       "[185950 rows x 9 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What city sold the most product?\n",
    "df2 = df[\"Purchase Address\"].apply(lambda x: x.split(\",\"))\n",
    "df['city']=df2.apply(lambda x: \" \".join(x[1].split()))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>8.262204e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>5.452571e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>4.664317e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston</th>\n",
       "      <td>3.661642e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atlanta</th>\n",
       "      <td>2.795499e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas</th>\n",
       "      <td>2.767975e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <td>2.747755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland</th>\n",
       "      <td>2.320491e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin</th>\n",
       "      <td>1.819582e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Sales\n",
       "city                       \n",
       "San Francisco  8.262204e+06\n",
       "Los Angeles    5.452571e+06\n",
       "New York City  4.664317e+06\n",
       "Boston         3.661642e+06\n",
       "Atlanta        2.795499e+06\n",
       "Dallas         2.767975e+06\n",
       "Seattle        2.747755e+06\n",
       "Portland       2.320491e+06\n",
       "Austin         1.819582e+06"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city').sum()[['Sales']].sort_values(by='Sales',ascending=False)\n",
    "#San franscisco in the 1st place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "df.groupby('city').sum()[['Sales']].plot.bar(ax=ax)\n",
    "plt.xticks(rotation=20)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Month</th>\n",
       "      <th>city</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Boston</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  \\\n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90   \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99   \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00   \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99   \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99   \n",
       "...                ...                                      ...     ...   \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97   \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00   \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00   \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99   \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95   \n",
       "\n",
       "        Month           city  Hour  \n",
       "0           4         Dallas     8  \n",
       "2           4         Boston    22  \n",
       "3           4    Los Angeles    14  \n",
       "4           4    Los Angeles    14  \n",
       "5           4    Los Angeles     9  \n",
       "...       ...            ...   ...  \n",
       "186845      9    Los Angeles    20  \n",
       "186846      9  San Francisco    16  \n",
       "186847      9  San Francisco     7  \n",
       "186848      9  San Francisco    17  \n",
       "186849      9  San Francisco     0  \n",
       "\n",
       "[185950 rows x 10 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's figure out the time when the customer is more likely(maximum likelihood) to buy a product\n",
    "df['Hour'] = pd.to_datetime(df['Order Date']).dt.hour\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Month</th>\n",
       "      <th>city</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Boston</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  \\\n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90   \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99   \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00   \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99   \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99   \n",
       "...                ...                                      ...     ...   \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97   \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00   \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00   \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99   \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95   \n",
       "\n",
       "        Month           city  Hour  \n",
       "0           4         Dallas     8  \n",
       "2           4         Boston    22  \n",
       "3           4    Los Angeles    14  \n",
       "4           4    Los Angeles    14  \n",
       "5           4    Los Angeles     9  \n",
       "...       ...            ...   ...  \n",
       "186845      9    Los Angeles    20  \n",
       "186846      9  San Francisco    16  \n",
       "186847      9  San Francisco     7  \n",
       "186848      9  San Francisco    17  \n",
       "186849      9  San Francisco     0  \n",
       "\n",
       "[185950 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy=df.copy()\n",
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sales\n",
       "Hour       \n",
       "19    12905\n",
       "12    12587\n",
       "11    12411\n",
       "18    12280\n",
       "20    12228\n",
       "13    12129\n",
       "14    10984\n",
       "10    10944\n",
       "21    10921\n",
       "17    10899\n",
       "16    10384\n",
       "15    10175\n",
       "22     8822\n",
       "9      8748\n",
       "23     6275\n",
       "8      6256\n",
       "7      4011\n",
       "0      3910\n",
       "6      2482\n",
       "1      2350\n",
       "5      1321\n",
       "2      1243\n",
       "4       854\n",
       "3       831"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Hour').count()[['Sales']].sort_values(by='Sales',ascending=False)\n",
    "# I recommand 19 & 11-12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Month</th>\n",
       "      <th>city</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Boston</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  \\\n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90   \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99   \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00   \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99   \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99   \n",
       "...                ...                                      ...     ...   \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97   \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00   \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00   \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99   \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95   \n",
       "\n",
       "        Month           city  Hour  \n",
       "0           4         Dallas     8  \n",
       "2           4         Boston    22  \n",
       "3           4    Los Angeles    14  \n",
       "4           4    Los Angeles    14  \n",
       "5           4    Los Angeles     9  \n",
       "...       ...            ...   ...  \n",
       "186845      9    Los Angeles    20  \n",
       "186846      9  San Francisco    16  \n",
       "186847      9  San Francisco     7  \n",
       "186848      9  San Francisco    17  \n",
       "186849      9  San Francisco     0  \n",
       "\n",
       "[185950 rows x 10 columns]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "x=list(range((df['Hour'].max()+1)))\n",
    "print(x)\n",
    "plt.plot(x,df.groupby('Hour').count()[['Sales']])\n",
    "plt.xticks(x)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USB-C Charging Cable          21903\n",
       "Lightning Charging Cable      21658\n",
       "AAA Batteries (4-pack)        20641\n",
       "AA Batteries (4-pack)         20577\n",
       "Wired Headphones              18882\n",
       "Apple Airpods Headphones      15549\n",
       "Bose SoundSport Headphones    13325\n",
       "27in FHD Monitor               7507\n",
       "iPhone                         6842\n",
       "27in 4K Gaming Monitor         6230\n",
       "34in Ultrawide Monitor         6181\n",
       "Google Phone                   5525\n",
       "Flatscreen TV                  4800\n",
       "Macbook Pro Laptop             4724\n",
       "ThinkPad Laptop                4128\n",
       "20in Monitor                   4101\n",
       "Vareebadd Phone                2065\n",
       "LG Washing Machine              666\n",
       "LG Dryer                        646\n",
       "Name: Product, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What products are most often sold ?\n",
    "df['Product'].value_counts(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "AAA Batteries (4-pack)        31017\n",
       "AA Batteries (4-pack)         27635\n",
       "USB-C Charging Cable          23975\n",
       "Lightning Charging Cable      23217\n",
       "Wired Headphones              20557\n",
       "Apple Airpods Headphones      15661\n",
       "Bose SoundSport Headphones    13457\n",
       "27in FHD Monitor               7550\n",
       "iPhone                         6849\n",
       "27in 4K Gaming Monitor         6244\n",
       "34in Ultrawide Monitor         6199\n",
       "Google Phone                   5532\n",
       "Flatscreen TV                  4819\n",
       "Macbook Pro Laptop             4728\n",
       "ThinkPad Laptop                4130\n",
       "20in Monitor                   4129\n",
       "Vareebadd Phone                2068\n",
       "LG Washing Machine              666\n",
       "LG Dryer                        646\n",
       "Name: Quantity Ordered, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#which product people need the most?\n",
    "df.groupby('Product').sum()['Quantity Ordered'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18]), <a list of 19 Text xticklabel objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,10])\n",
    "sns.barplot(x=\"Product\", y=\"Quantity Ordered\",data=df)\n",
    "plt.xticks(rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "AAA Batteries (4-pack)           2.99\n",
       "AA Batteries (4-pack)            3.84\n",
       "USB-C Charging Cable            11.95\n",
       "Wired Headphones                11.99\n",
       "Lightning Charging Cable        14.95\n",
       "Bose SoundSport Headphones      99.99\n",
       "20in Monitor                   109.99\n",
       "27in FHD Monitor               149.99\n",
       "Apple Airpods Headphones       150.00\n",
       "Flatscreen TV                  300.00\n",
       "34in Ultrawide Monitor         379.99\n",
       "27in 4K Gaming Monitor         389.99\n",
       "Vareebadd Phone                400.00\n",
       "Google Phone                   600.00\n",
       "LG Washing Machine             600.00\n",
       "LG Dryer                       600.00\n",
       "iPhone                         700.00\n",
       "ThinkPad Laptop                999.99\n",
       "Macbook Pro Laptop            1700.00\n",
       "Name: Price Each, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.groupby('Product').mean()['Price Each'].sort_values(ascending=True)\n",
    "#the product that are sold the most have the less prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'average price per product')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(df.groupby('Product').mean()['Price Each'],marker='o')\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid()\n",
    "axis.set_title('average price per product')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df[df['Order ID'].duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>order_grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone + Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>176562</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178437 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                    order_grouped\n",
       "0        176558             USB-C Charging Cable\n",
       "2        176559       Bose SoundSport Headphones\n",
       "3        176560  Google Phone + Wired Headphones\n",
       "5        176561                 Wired Headphones\n",
       "6        176562             USB-C Charging Cable\n",
       "...         ...                              ...\n",
       "186845   259353           AAA Batteries (4-pack)\n",
       "186846   259354                           iPhone\n",
       "186847   259355                           iPhone\n",
       "186848   259356           34in Ultrawide Monitor\n",
       "186849   259357             USB-C Charging Cable\n",
       "\n",
       "[178437 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what product often sold together?\n",
    "## Referenced: https://stackoverflow.com/questions/27298178/concatenate-strings-from-several-rows-using-pandas-groupby\n",
    "df['order_grouped']=df.groupby('Order ID')['Product'].transform(lambda x:' + '.join(x))\n",
    "df=df[['Order ID','order_grouped']].drop_duplicates()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lightning Charging Cable      19831\n",
       "AAA Batteries (4-pack)        19826\n",
       "AA Batteries (4-pack)         19809\n",
       "USB-C Charging Cable          19792\n",
       "Wired Headphones              17208\n",
       "Apple Airpods Headphones      14575\n",
       "Bose SoundSport Headphones    12505\n",
       "27in FHD Monitor               7213\n",
       "27in 4K Gaming Monitor         5979\n",
       "34in Ultrawide Monitor         5935\n",
       "Name: order_grouped, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_grouped'].value_counts(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>order_grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone + Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>176562</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178437 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                    order_grouped\n",
       "0        176558             USB-C Charging Cable\n",
       "2        176559       Bose SoundSport Headphones\n",
       "3        176560  Google Phone + Wired Headphones\n",
       "5        176561                 Wired Headphones\n",
       "6        176562             USB-C Charging Cable\n",
       "...         ...                              ...\n",
       "186845   259353           AAA Batteries (4-pack)\n",
       "186846   259354                           iPhone\n",
       "186847   259355                           iPhone\n",
       "186848   259356           34in Ultrawide Monitor\n",
       "186849   259357             USB-C Charging Cable\n",
       "\n",
       "[178437 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "#this is a good strategoy for an upcoming promotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Month</th>\n",
       "      <th>city</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day of week</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Boston</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>8.97</td>\n",
       "      <td>9</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>700.00</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>379.99</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address   Sales  \\\n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001   23.90   \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215   99.99   \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  600.00   \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001   11.99   \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001   11.99   \n",
       "...                ...                                      ...     ...   \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001    8.97   \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  700.00   \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  700.00   \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  379.99   \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016   11.95   \n",
       "\n",
       "        Month           city  Hour  day of week  year  \n",
       "0           4         Dallas     8            4  2019  \n",
       "2           4         Boston    22            6  2019  \n",
       "3           4    Los Angeles    14            4  2019  \n",
       "4           4    Los Angeles    14            4  2019  \n",
       "5           4    Los Angeles     9            1  2019  \n",
       "...       ...            ...   ...          ...   ...  \n",
       "186845      9    Los Angeles    20            1  2019  \n",
       "186846      9  San Francisco    16            6  2019  \n",
       "186847      9  San Francisco     7            0  2019  \n",
       "186848      9  San Francisco    17            3  2019  \n",
       "186849      9  San Francisco     0            0  2019  \n",
       "\n",
       "[185950 rows x 12 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#which day of the week people tend to buy the most?\n",
    "df_copy['day of week'] = pd.to_datetime(df_copy['Order Date']).dt.dayofweek\n",
    "df_copy['year'] = pd.to_datetime(df_copy['Order Date']).dt.year\n",
    "df_copy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Order Date']=pd.to_datetime(df_copy['Order Date'])\n",
    "df_copy['Order Date'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 6, 1, 0, 2, 3, 5], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def week_end(row):\n",
    "    if row == 5 or row == 6:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "df_copy['Weekend']=df_copy['day of week'].apply(week_end)\n",
    "df_copy['day of week'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Sales Per Day of the Week')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis = df_copy.groupby('day of week')[['Sales']].mean().plot(figsize = (10,5), marker = 'o', color = 'r')\n",
    "axis.set_title('Average Sales Per Day of the Week')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x221b86909c8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+eJK/TfJ14OWL/MeQpmXctVxdB2wCSPIU4DLgIWAdcC6wHnhJkld0+7+5ql7C4KML357ktG79mcDuqnpZVd21mH8A6Xh8haqWpe4Vk48kOQdYBfwn8FLgVd1lgBUMYv9VBkG/pFtf260/Ahxl8IHP0pOKcddy9gngT4HfAK5n8MHNf19V/zy8U/ey+d8HXl5VP03yFeAZ3eb/qaqjizWwNFueltFy9nngIgZH7F/qvt6cZAVAktVJTgeeDTzWhf2FwHl9DSzNlkfuWraq6udJ7gR+2B19fznJi4CvJQF4HPgT4Hbgz5PsAu4Htvc1szRbvv2Alq3uF6n3ApdW1QN9zyMtJE/LaFlKchawD9hm2NUij9wlqUEeuUtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXo/wAZVtMGMDJ6+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_copy.groupby('year')['Sales'].mean().plot.bar()\n",
    "#our sales in 2020 is higher,(we're gonna forcast the sales after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x221ae1bed88>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_copy.groupby('Weekend')['Sales'].mean().plot.bar()\n",
    "#we are not getting much insights from weekend days ,so the wheither the day is weekend or not doesn't affect sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sales forcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "data=df_copy[['Order Date','Sales']]\n",
    "data.to_csv('date_sales_Data.csv',index=[0],parse_dates=True,squeeze=True)\n",
    "\"\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_forcast=pd.read_csv('date_sales_Data.csv')\n",
    "df_forcast['Order Date'] = pd.to_datetime(df_forcast['Order Date'])\n",
    "df_forcast.set_index('Order Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Date\n",
       "2019-12-31    4.613443e+06\n",
       "2019-10-31    3.736727e+06\n",
       "2019-04-30    3.390670e+06\n",
       "2019-11-30    3.199603e+06\n",
       "2019-05-31    3.152607e+06\n",
       "2019-03-31    2.807100e+06\n",
       "2019-07-31    2.647776e+06\n",
       "2019-06-30    2.577802e+06\n",
       "2019-08-31    2.244468e+06\n",
       "2019-02-28    2.202022e+06\n",
       "2019-09-30    2.097560e+06\n",
       "2019-01-31    1.813586e+06\n",
       "2020-01-31    8.670290e+03\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show data by month:\n",
    "y=df_forcast['Sales'].resample('M').sum()\n",
    "y.sort_values(ascending =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.plot(figsize=(15, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Sales</th>\n",
       "      <th>Predicted Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2.202022e+06</td>\n",
       "      <td>1.813586e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>2.807100e+06</td>\n",
       "      <td>2.202022e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>3.390670e+06</td>\n",
       "      <td>2.807100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>3.152607e+06</td>\n",
       "      <td>3.390670e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2.577802e+06</td>\n",
       "      <td>3.152607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>2.647776e+06</td>\n",
       "      <td>2.577802e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-31</th>\n",
       "      <td>2.244468e+06</td>\n",
       "      <td>2.647776e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>2.097560e+06</td>\n",
       "      <td>2.244468e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>3.736727e+06</td>\n",
       "      <td>2.097560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>3.199603e+06</td>\n",
       "      <td>3.736727e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>4.613443e+06</td>\n",
       "      <td>3.199603e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>8.670290e+03</td>\n",
       "      <td>4.613443e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual Sales  Predicted Sales\n",
       "Order Date                               \n",
       "2019-02-28  2.202022e+06     1.813586e+06\n",
       "2019-03-31  2.807100e+06     2.202022e+06\n",
       "2019-04-30  3.390670e+06     2.807100e+06\n",
       "2019-05-31  3.152607e+06     3.390670e+06\n",
       "2019-06-30  2.577802e+06     3.152607e+06\n",
       "2019-07-31  2.647776e+06     2.577802e+06\n",
       "2019-08-31  2.244468e+06     2.647776e+06\n",
       "2019-09-30  2.097560e+06     2.244468e+06\n",
       "2019-10-31  3.736727e+06     2.097560e+06\n",
       "2019-11-30  3.199603e+06     3.736727e+06\n",
       "2019-12-31  4.613443e+06     3.199603e+06\n",
       "2020-01-31  8.670290e+03     4.613443e+06"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#baseline model:\n",
    "df_base=pd.concat([y,y.shift(1)],axis=1)\n",
    "df_base.columns=['Actual Sales','Predicted Sales']\n",
    "df_base.dropna(inplace=True)\n",
    "df_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1516905.1207953186"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(df_base['Actual Sales'],df_base['Predicted Sales']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Arima model:\n",
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.641661671707729,\n",
       " 0.0001081586748837491,\n",
       " 2,\n",
       " 10,\n",
       " {'1%': -4.331573, '5%': -3.23295, '10%': -2.7487},\n",
       " 243.38706614869884)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "adf_test=adfuller(y)\n",
    "adf_test\n",
    "#p-value<0.05 data is stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xftjwuLABAAAEEABnq/dE2PDkXSkeOzmXZiQFAAA8hQAC4DS01vLoTH+CyK8OwoZHZ4QNAABwugQQAIu01vLYyfleDfO3wZxNT9gAAABnTQABnPcePzmXrz7+5ASRj87MZU7aAAAAIyWAAM4rj5+c79Ew6OEwM5vZOWEDAAB0TQABbFgzs70nJoecfz4pbAAAgLEQQMAaNNdrme310utlwfPc8IyHp/E5eqVdTmcCxXYaF1rpPKfzkb+tcJIzmezx0Zl+74aZWbe/BACAtUIAASPS67XM9lp6rQ0ChLagbX59brht7sltc4NHrzV3VgAAADYcAQTntdYWBQLDoUGvZW5RSDC/bW6J/YUGAAAAyxNAsC4tNURhri09bKHfI+Gpbf1gYdw/CQAAwPlBAME5M9+jYK639BCFud6ibUO9D+YW7ae3AQAAwPoigOCUlhui8ERYsMQQhSXbenobAAAAnM8EEOexLxx5dNAjobfkEIW5Xn+oAwAAAKyWAOI89udHHxt3CQAAAJwnJsZdAAAAALDxCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzo0kgKiqV1TV56rqQFW9fontP1RVD1fV/sHjtUPbbqyqewePG0dRDwAAALC2rPo2nFU1meQdSV6e5IEkn6iq21trdy/a9ddaa69bdOwzk/xMkr1JWpJPDo59ZLV1AQAAAGvHKHpAvCDJgdbafa21mSTvTXL9aR77nUk+3Fr78iB0+HCSV4ygJgAAAGANGUUAcVmSQ0PrDwzaFvtfq+pTVfW+qtp5hscCAAAA69goAohaoq0tWv+dJLtaa9+U5PeS3HYGx/Z3rLqpqvZV1b6HH374rIsFAAAAzr1RBBAPJNk5tP7cJA8O79BaO9JaOzFY/fdJ/sfTPXboHLe01va21vZecsklIygbAAAAOFdGEUB8IslVVXVlVU0neVWS24d3qKpLh1ZfmeSewfKHknxHVW2rqm1JvmPQBgAAAGwgq74LRmtttqpel35wMJnk1tbaZ6vqTUn2tdZuT/KjVfXKJLNJvpzkhwbHfrmq/nn6IUaSvKm19uXV1gQAAACsLasOIJKktfaBJB9Y1PbTQ8tvSPKGZY69Ncmto6gDAAAAWJtGMQQDAAAA4JQEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnRhJAVNUrqupzVXWgql6/xPZ/WFV3V9WnquqOqrpiaNtcVe0fPG4fRT0AAADA2jK12hNU1WSSdyR5eZIHknyiqm5vrd09tNufJNnbWnu0qv5+kv8ryfcNtj3WWtuz2joAAACAtWsUPSBekORAa+2+1tpMkvcmuX54h9ba77fWHh2sfizJc0dwXQAAAGCdGEUAcVmSQ0PrDwzalvOaJB8cWr+wqvZV1ceq6oYR1AMAAACsMasegpGklmhrS+5Y9beT7E3yV4eaL2+tPVhVX5vkzqr6dGvtz5Y49qYkNyXJ5ZdfvvqqAQAAgHNmFD0gHkiyc2j9uUkeXLxTVb0syRuTvLK1dmK+vbX24OD5viQfSfL8pS7SWrultba3tbb3kksuGUHZAAAAwLkyigDiE0muqqorq2o6yauSLLibRVU9P8k70w8fDg+1b6uqTYPli5O8KMnw5JUAAADABrDqIRittdmqel2SDyWZTHJra+2zVfWmJPtaa7cn+ddJtiT5j1WVJF9orb0yydVJ3llVvfTDkLcuunsGAAAAsAGMYg6ItNY+kOQDi9p+emj5Zcsc98dJvnEUNQAAAABr1yiGYAAAAACckgACAAAA6JwAAgAAAOicAAIAAADonAACAAAA6JwAAgAAAOicAAIAAADonAACAAAA6JwAAgAAAOicAAIAAADonAACAAAA6JwAAgAAAOicAAIAAADo3NS4CwAA2Gh6vZb9h47m4JHj2bV9c/bs3JqJiRp3WQAwVgIIAIAR6vVa3vLBe3Lg8LHMzPYyPTWR3Tu25ObrrhZCAHBeMwQDAGCE9h86mgOHj+XEbC8tyYnZXg4cPpb9h46OuzQAltDrtdx1/yP5zbseyF33P5Jer427pA1LDwgAgBE6eOR4ZmZ7C9pmZns5eOR4rr1i25iqAmApeq2dW3pAAACM0K7tmzM9tfBPrOmpiezavnlMFQGwHL3Wzi0BBADACO3ZuTW7d2xJZmeS1sumwbdpe3ZuHXdpACxyql5rjJ4AAgBghCYmKjdfd3W23P2fctHn/zA/+pKrdOUFWKP0Wju3BBAAACM2MVGZPnIgF93/R7n2im3CB4A1Sq+1c0sAAQAAwHlJr7VzSwABAADAeUuvtXNHAAEAAAB0TgABAAAAdE4AAQAAAHROAAEAAAB0TgABAAAAdE4AAQAAAHROAAEAAAB0TgABAAAAdE4AAQAAAHRuatwFAMBa0uu17D90NAePHM+u7ZuzZ+fWTEzUuMsCAFj3BBAAMNDrtbzlg/fkwOFjmZntZXpqIrt3bMnN110thAAAWCVDMABgYP+hozlw+FhOzPbSkpyY7eXA4WPZf+jouEtbU3q9lrvufyS/edcDuev+R9LrtXGXBACsA3pAAMDAwSPHMzPbW9A2M9vLwSPHc+0V28ZU1dqilwgAcLb0gACAgV3bN2d6auGvxumpiezavnlMFa09eokAAGdLAAEAA3t2bs3uHVuS2Zmk9bJp8O3+np1bx13amnGqXiIAAKcigACAgYmJys3XXZ0td/+nXPT5P8yPvuQqQwsW0UsEADhbAggAGDIxUZk+ciAX3f9HufaKbcKHRfQSAQDOlgACADhteokAAGdLAAEAnBG9RACAsyGAAAAAADongAAAAAA6J4AAAAAAOieAAAAAADongAAAAAA6J4AAAAAAOjc17gIAADj/9Hot+w8dzcEjx7Nr++bs2bnVLV0BNjgBBAAA51Sv1/KWD96TA4ePZWa2l+mpiezesSU3X3e1EAJgAxvJEIyqekVVfa6qDlTV65fYvqmqfm2w/eNVtWto2xsG7Z+rqu8cRT0AAKxd+w8dzYHDx3JitpeW5MRsLwcOH8v+Q0fHXRoAHVp1AFFVk0nekeS6JNckeXVVXbNot9ckeaS1tjvJ25L8q8Gx1yR5VZKvT/KKJP92cD4AADaog0eOZ2a2t6BtZraXg0eOj6kiAM6FUfSAeEGSA621+1prM0nem+T6Rftcn+S2wfL7kry0qmrQ/t7W2onW2ueTHBicDwCADWrX9s2Znlr4Z+j01ER2bd88pooAOBdGMQfEZUkODa0/kOSFy+3TWputqr9Msn3Q/rFFx1620gXve/h4vu+dH11NzefUQ9d8X5KsuZq/8vjJcZcAsCZ9Zc/3J0ne9P7PjrmStctrtDKv0fJaa5mcqKT1klRqojI5UfmdT/153v/pB8ddHnAeWov/Z09PTuTCCzbWAIFRBBBLzRTUTnOf0zm2f4Kqm5LclCRbLv26M6lv7PZ8855xl7Au3Hv3Z5IkV13zDWOuZO3yGq3Ma7Qyr9HKvDYr8xqtzGu0vKrK5c+8KMdOTOfEyblsumAyWzZNpt9BlmH+z16Z12hlXqOVeW3OjWptyc/7p3+Cqm9N8k9ba985WH9DkrTW/uXQPh8a7PPRqppK8sUklyR5/fC+w/ud6pp79+5t+/btW1XdJB/9syPjLmGBH/5br0ySvONXbx9zJWuX12hlXqOVeY0A1g//Z6/Ma7Qyr9H69OyvuTBXXrz+hqZV1Sdba3uX2jaKOSA+keSqqrqyqqbTn1Ry8Tv79iQ3Dpa/J8mdrZ983J7kVYO7ZFyZ5Kok/3UENQEAAABryKqHYAzmdHhdkg8lmUxya2vts1X1piT7Wmu3J/nFJL9cVQeSfDn9kCKD/X49yd1JZpP8cGttbrU1AbC0Xq9lZvvuzG15Vu66/5Hs2bk1ExO6PAMA0L1RzAGR1toHknxgUdtPDy0/nuRvLnPsm5O8eRR1ALC8Xq/lLR+8J8euuSGZnMrb77w3u3dsyc3XXS2EAIANypcPrCWjGIIBnAPzvzweu+JFuev+R9LrrW7+Fs4/+w8dzYHDx5Kp6aQmcmK2lwOHj2X/oaPjLg0A6MDwlw+PXfltefud9+YtH7zH35GMjQAC1gG/PBiFg0eOZ2a2t6BtZraXg0eOj6kiAKBLvnxgrRFAwDrglwejsGv75kxPLfxvf3pqIru2r7/ZlQGAlfnygbVGAAHrgF8ejMKenVuze8eWbJqaSCXZNDWR3Tu2ZM/OreMuDQDogC8fWGtGMgkl0K35Xx4nhkIIvzw4UxMTlZuvuzr7Dx3NwSPHs2v7ZhNRAcAGNv/lw4HDxzIz28u0Lx8YMwEErAN+eTAqExOVa6/Ylmuv2DbuUgCAjvnygbVGAAHrgF8eAACcDV8+sJYIIGCd8MsDAABYz0xCCQAAAHROAAEAAAB0TgABAAAAdE4AAQAAAHROAAEAAAB0TgABAAAAdE4AAQAAAHROAAEAAAB0TgABAAAAdE4AAQAAAHROAAEAAAB0TgABAAAAdE4AAQAArEu9XsvM9t157IoX5a77H0mv18ZdEnAKU+MuAAAA4Ez1ei1v+eA9OXbNDcnkVN5+573ZvWNLbr7u6kxM1LjLA5agBwQAALDu7D90NAcOH0umppOayInZXg4cPpb9h46OuzRgGQIIAABg3Tl45HhmZnsL2mZmezl45PiYKgJWIoAAAADWnV3bN2d6auHHmempiezavnlMFQErEUAAAADrzp6dW7N7x5ZsmppIJdk0NZHdO7Zkz86t4y4NWIZJKAEAgHVnYqJy83VXZ/+hozl45Hh2bd+cPTu3moAS1jABBAAAsC5NTFSuvWJbrr1i27hLAU6DIRgAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAACwBvV6LTPbd+exK16Uu+5/JL1eG3dJAKsyNe4CAACAhXq9lrd88J4cu+aGZHIqb7/z3uzesSU3X3d1JiZq3OUBnBU9IAAAYI3Zf+hoDhw+lkxNJzWRE7O9HDh8LPsPHR13aQBnTQABAABrzMEjxzMz21vQNjPby8Ejx8dUEcDqCSAAAGCN2bV9c6anFv6pPj01kV3bN4+pIoDVE0CwJphkCQDgSXt2bs3uHVuyaWoilWTT1ER279iSPTu3jrs0gLNmEkrGziRLAAALTUxUbr7u6uw/dDQHjxzPru2bs2fnVn8bAeuaAIKxWzDJUrJgkqVrr9g25uoAAMZjYqJy7RXb/D0EbBiGYDB2JlkCAADY+AQQjJ1JlgAAADY+AQRjZ5IlAACAjc8cEIydSZYAAAA2vlX1gKiqZ1bVh6vq3sHzU2bIqao9VfXRqvpsVX2qqr5vaNu7qurzVbV/8NizmnpYv+YnWfrua5+ba6/YJnwAAADYYFY7BOP1Se5orV2V5I7B+mKPJvnB1trXJ3lFkp+tquG+9f+4tbZn8Ni/ynoAAACANWi1AcT1SW4bLN+W5IbFO7TW/rS1du9g+cEkh5NcssrrAgAAAOvIagOIZ7XWHkqSwfOOU+1cVS9IMp3kz4aa3zwYmvG2qtq0ynoAAACANWjFSSir6veSPHuJTW88kwtV1aVJfjnJja213qD5DUm+mH4ocUuSn0zypmWOvynJTUly+eWXn8mlAQAAgDFbMYBorb1suW1V9aWqurS19tAgYDi8zH7PSPKfk8RLB/QAAA/2SURBVPxUa+1jQ+d+aLB4oqp+KclPnKKOW9IPKbJ37962Ut0AAADA2rHaIRi3J7lxsHxjkt9evENVTSf5rST/obX2Hxdtu3TwXOnPH/GZVdYDAAAArEGrDSDemuTlVXVvkpcP1lNVe6vqFwb7fG+Sb0/yQ0vcbvPdVfXpJJ9OcnGSf7HKegAAAIA1aMUhGKfSWjuS5KVLtO9L8trB8q8k+ZVljn/Jaq4PAAAArA+r7QEBAAAAsCIBBAAAANA5AQQAAADQOQEEAAAA0DkBBAAAANA5AQQAAADQOQEEAAAA0DkBBAAAANA5AQQAAADQOQEEAAAA0DkBBAAAANA5AQQAAADQOQEEAAAA0DkBBAAAANA5AQQAAADQOQEEAAAA0DkBBAAAANA5AQQAAADQOQEEsGH0ei0z23fnsStelLvufyS9Xht3SQAAwMDUuAsAGIVer+UtH7wnx665IZmcytvvvDe7d2zJzdddnYmJGnd5AABw3tMDAtgQ9h86mgOHjyVT00lN5MRsLwcOH8v+Q0fHXRoAABABBLBBHDxyPDOzvQVtM7O9HDxyfEwVAQAAwwQQwIawa/vmTE8t/C9temoiu7ZvHlNFAADAMAEEsCHs2bk1u3dsyaapiVSSTVMT2b1jS/bs3Dru0gAAgJiEEtggJiYqN193dfYfOpqDR45n1/bN2bNzqwkoAQBgjRBAABvGxETl2iu25dorto27FAAAYBFDMAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM4JIAAAAIDOCSAAAACAzgkgAAAAgM6tKoCoqmdW1Yer6t7B87Zl9purqv2Dx+1D7VdW1ccHx/9aVU2vph4AAABgbVptD4jXJ7mjtXZVkjsG60t5rLW2Z/B45VD7v0rytsHxjyR5zSrrAQAAANag1QYQ1ye5bbB8W5IbTvfAqqokL0nyvrM5HgAAAFg/VhtAPKu19lCSDJ53LLPfhVW1r6o+VlXzIcP2JEdba7OD9QeSXLbKegAAAIA1aGqlHarq95I8e4lNbzyD61zeWnuwqr42yZ1V9ekkX1liv3aKOm5KclOSXH755WdwaQAAAGDcVgwgWmsvW25bVX2pqi5trT1UVZcmObzMOR4cPN9XVR9J8vwkv5Fka1VNDXpBPDfJg6eo45YktyTJ3r17lw0qAAAAgLVntUMwbk9y42D5xiS/vXiHqtpWVZsGyxcneVGSu1trLcnvJ/meUx0PAAAArH+rDSDemuTlVXVvkpcP1lNVe6vqFwb7XJ1kX1X9t/QDh7e21u4ebPvJJP+wqg6kPyfEL66yHgAAAGANWnEIxqm01o4keekS7fuSvHaw/MdJvnGZ4+9L8oLV1AAAAACsfavtAQEAAACwIgEEAAAA0DkBBAAAANA5AQQAAADQOQEEAAAA0LlV3QWD9e2qZ21Jr9cy22uZm3+0oeUl2lobd9UAAACsRwKI89jFWzad8THzQUSvLQouhsKKp4Qay4QbAAAAnD8EEJyRyYnK5ESt+jytPTWU6PWS2V5vyV4YywYevRZZBgAAwNongGAsqipTkzWSN2Bvce+K1jI3t/JwEkNMAAAAzh0BBOvexERlIpULJld/rtMKK04ZbvQy14shJgAAAIsIIGBIl0NMThlsDNpn5/rDTQwxAQAANhoBBHSgyyEm804nl2grjCk5vXOczk4rbV75JCtd53R/3pbksZm5HJ+ZzfETs5mZleAAAMBaIICANW6UQ0zOR4+fnMvxE7M5fmIux07M5vjMbGbnhBIAAHCuCSCADe3CCyZz4QWT2b7lybbHTw7CiBOzg+c583YAAEDHBBDAeWc+lLh4y6Yk/aEbj5/s5asnTub4ifkeE7Pm3wAAgBESQADnvarKRdOTuWh6Mnl6v621lkdn5hb0kjg+M+tWrQAAcJYEEABLqKps3jSVzZumsmPQ1uu1weSWTw7heOzknFACAABOgwAC4DRNTFSefuEFefqFFzzRNvdEKDE/p8RcHpuZG2OVAACwNgkgAFZhcqLyjAsvyDOGQonZuV6Ozyyc6PLEyd4YqwQAgPETQACM2NTkRL7mool8zUVPhhIn53oL5pM4dmI2M7NCCQAAzh8CCIBz4ILJiWx92nS2Pm36ibaZ2SdDifneEifnTCgBAMDGJIAAGJPpqYlMT01n2+YnQ4nHT87fBnQwhGNmNrNCCQAANgABBMAacuEFk7nwgsls3/Jk2+Mn+2HEscf7PSUenZnLXE8oAQDA+iKAAFjj5kOJi7dsSpK01vLYybkn5pOYvwOHTAIAgLVMAAGwzlRVnjY9ladNTyVP77e11vLozNyCOSUenZlLE0oAALBGCCAANoCqyuZNU9m8aSo7Bm29XsvxmaH5JE7M5rGTQgkAAMZDAAGwQU1MVJ5+4QV5+oVP3g50bhBK9FY5XmM1IcZqrtxWmZ6cydHzl2ppTxzYhra1wVob2jZf33CZw+dZct8F+z15fDvV8Uu0PaXmp5ynPeWcWVTTE8cM1SawAgBGRQABcB6ZnKg8YyiQgNM1HK60xW1PrA+eF4UzWWH7SufLWR7X0hYdf4Z1jKT+lrle0mstc71+4DPXWnqtpddr6bX+NkEPAOcDAQQAsKKqGjwvaB1LLRtRr9eGgolBYNFa2tDygm3LhBlzvcF6e3K9DS0DwDgJIAAAxmxiojJxDgKdUwUdTwkznlh+sqfGSkHH3GA7ACxFAAEAcJ44V0HHfECxuKfGUkHHk7OTrN5GHcoy/1o+8Wgts3P913G2138tZ3uG8gBrnwACAICRmpyoTKZyweS4Kzm/zPdwmQ8qhsOJxWHF3OJHa5nr9TLXM1wH6I4AAgAANoD5Hi6rDX7mh9PMLe55sUIPjOHtemUASxFAAAAAT6iqTE3WSD4oLO6BsWTPi7mFPTBme730BsN19MqAjUUAAQAAdGJiojI9sfp5R+Z7ZSw1nGTBLW0H+7Y2f9vgwVwji7flyVvgtsGx/essPE9vcDvdfvvweVb9I8F5SQABAACsaU/0ylgj84osGWTkFMFHb2j76Rwz1LYwFFkYgpzWtYdqPbufdTSv1yiM4izCo/ESQAAAAJyBqkoNOnZMnoM7y7D2jCJUWekUtQHfWgIIAAAAOAM1gnRgIwYMK5kYdwEAAADAxieAAAAAADongAAAAAA6J4AAAAAAOieAAAAAADongAAAAAA6J4AAAAAAOieAAAAAADongAAAAAA6J4AAAAAAOieAAAAAADongAAAAAA6t6oAoqqeWVUfrqp7B8/bltjnr1XV/qHH41V1w2Dbu6rq80Pb9qymHgAAAGBtWm0PiNcnuaO1dlWSOwbrC7TWfr+1tqe1tifJS5I8muR3h3b5x/PbW2v7V1kPAAAAsAatNoC4Psltg+Xbktywwv7fk+SDrbVHV3ldAAAAYB1ZbQDxrNbaQ0kyeN6xwv6vSvKeRW1vrqpPVdXbqmrTKusBAAAA1qCplXaoqt9L8uwlNr3xTC5UVZcm+cYkHxpqfkOSLyaZTnJLkp9M8qZljr8pyU1Jcvnll5/JpQEAAIAxWzGAaK29bLltVfWlqrq0tfbQIGA4fIpTfW+S32qtnRw690ODxRNV9UtJfuIUddySfkiRvXv3tpXqBgAAANaOFQOIFdye5MYkbx08//Yp9n11+j0enjAUXlT680d85nQu+slPfvIvqur+syt5bC5O8hfjLoINwXuJUfA+YhS8jxgF7yNGwfuIUfA+Go0rlttQrZ19Z4Kq2p7k15NcnuQLSf5ma+3LVbU3yd9rrb12sN+uJH+UZGdrrTd0/J1JLklSSfYPjjl21gWtYVW1r7W2d9x1sP55LzEK3keMgvcRo+B9xCh4HzEK3kfdW1UPiNbakSQvXaJ9X5LXDq0fTHLZEvu9ZDXXBwAAANaH1d4FAwAAAGBFAohz55ZxF8CG4b3EKHgfMQreR4yC9xGj4H3EKHgfdWxVc0AAAAAAnA49IAAAAIDOCSDOgap6RVV9rqoOVNXrx10P609V7ayq36+qe6rqs1X1D8ZdE+tXVU1W1Z9U1fvHXQvrU1Vtrar3VdV/H/y/9K3jron1p6p+fPA77TNV9Z6qunDcNbE+VNWtVXW4qj4z1PbMqvpwVd07eN42zhpZ+5Z5H/3rwe+2T1XVb1XV1nHWuBEJIDpWVZNJ3pHkuiTXJHl1VV0z3qpYh2aT/KPW2tVJviXJD3sfsQr/IMk94y6Cde3/TfJfWmt/Jck3x/uJM1RVlyX50SR7W2vfkGQyyavGWxXryLuSvGJR2+uT3NFauyrJHYN1OJV35anvow8n+YbW2jcl+dMkbzjXRW10AojuvSDJgdbafa21mSTvTXL9mGtinWmtPdRau2uw/NX0/9h/yq1tYSVV9dwkfyPJL4y7FtanqnpGkm9P8otJ0lqbaa0dHW9VrFNTSS6qqqkkT0vy4JjrYZ1orf1Bki8var4+yW2D5duS3HBOi2LdWep91Fr73dba7GD1Y0mee84L2+AEEN27LMmhofUH4oMjq1BVu5I8P8nHx1sJ69TPJvknSXrjLoR162uTPJzklwZDeX6hqjaPuyjWl9banyf5v5N8IclDSf6ytfa7462Kde5ZrbWHkv4XN0l2jLke1r//LckHx13ERiOA6F4t0ebWI5yVqtqS5DeS/Fhr7Svjrof1paq+K8nh1tonx10L69pUkmuT/Hxr7flJjkdXZ87QYHz+9UmuTPKcJJur6m+PtyqAvqp6Y/pDoN897lo2GgFE9x5IsnNo/bnRxZCzUFUXpB8+vLu19pvjrod16UVJXllVB9MfDvaSqvqV8ZbEOvRAkgdaa/O9sN6XfiABZ+JlST7fWnu4tXYyyW8m+Z/GXBPr25eq6tIkGTwfHnM9rFNVdWOS70ry/a01XxyPmACie59IclVVXVlV0+lPsHT7mGtinamqSn+89T2ttf9n3PWwPrXW3tBae25rbVf6/xfd2VrzjSNnpLX2xSSHqup5g6aXJrl7jCWxPn0hybdU1dMGv+NeGpOZsjq3J7lxsHxjkt8eYy2sU1X1iiQ/meSVrbVHx13PRiSA6NhgEpPXJflQ+r9Yf7219tnxVsU69KIkP5D+N9b7B4+/Pu6igPPWjyR5d1V9KsmeJG8Zcz2sM4MeNO9LcleST6f/N+ktYy2KdaOq3pPko0meV1UPVNVrkrw1ycur6t4kLx+sw7KWeR/9XJKnJ/nw4O/tfzfWIjeg0qsEAAAA6JoeEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5wQQAAAAQOcEEAAAAEDnBBAAAABA5/5/cnbhxZ0QU4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=ARIMA(y,order=(1,0,0))\n",
    "model_fit=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "402.3637611458194"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Sales</td>      <th>  No. Observations:  </th>     <td>13</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(1, 0)</td>    <th>  Log Likelihood     </th>  <td>-198.182</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th> <td>1003898.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 06 Sep 2020</td> <th>  AIC                </th>   <td>402.364</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>01:25:35</td>     <th>  BIC                </th>   <td>404.059</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>01-31-2019</td>    <th>  HQIC               </th>   <td>402.015</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 01-31-2020</td>   <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td> 2.653e+06</td> <td> 2.32e+05</td> <td>   11.444</td> <td> 0.000</td> <td>  2.2e+06</td> <td> 3.11e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Sales</th> <td>   -0.3921</td> <td>    0.388</td> <td>   -1.010</td> <td> 0.334</td> <td>   -1.153</td> <td>    0.368</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -2.5505</td> <td>          +0.0000j</td> <td>           2.5505</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   No. Observations:                   13\n",
       "Model:                     ARMA(1, 0)   Log Likelihood                -198.182\n",
       "Method:                       css-mle   S.D. of innovations        1003898.774\n",
       "Date:                Sun, 06 Sep 2020   AIC                            402.364\n",
       "Time:                        01:25:35   BIC                            404.059\n",
       "Sample:                    01-31-2019   HQIC                           402.015\n",
       "                         - 01-31-2020                                         \n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const        2.653e+06   2.32e+05     11.444      0.000     2.2e+06    3.11e+06\n",
       "ar.L1.Sales    -0.3921      0.388     -1.010      0.334      -1.153       0.368\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -2.5505           +0.0000j            2.5505            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3690116.1308418 , 2246691.73130834, 2812630.79091763,\n",
       "       2590736.92567954, 2677737.26006941, 2643626.09536539,\n",
       "       2657000.4296783 , 2651756.6093671 , 2653812.61087064,\n",
       "       2653006.49206189, 2653322.55580635, 2653198.63326605,\n",
       "       2653247.22092048])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_forecast=model_fit.forecast(steps=13)[0]\n",
    "model_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Date\n",
       "2019-01-31    1.813586e+06\n",
       "2019-02-28    2.202022e+06\n",
       "2019-03-31    2.807100e+06\n",
       "2019-04-30    3.390670e+06\n",
       "2019-05-31    3.152607e+06\n",
       "2019-06-30    2.577802e+06\n",
       "2019-07-31    2.647776e+06\n",
       "2019-08-31    2.244468e+06\n",
       "2019-09-30    2.097560e+06\n",
       "2019-10-31    3.736727e+06\n",
       "2019-11-30    3.199603e+06\n",
       "2019-12-31    4.613443e+06\n",
       "2020-01-31    8.670290e+03\n",
       "Freq: M, Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1149505.6339384709"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "np.sqrt(mean_squared_error(model_forecast,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>1.813586e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>2.202022e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-31</td>\n",
       "      <td>2.807100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>3.390670e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>3.152607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>2.577802e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2.647776e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2.244468e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.097560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>3.736727e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-11-30</td>\n",
       "      <td>3.199603e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>4.613443e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>8.670290e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order Date         Sales\n",
       "0  2019-01-31  1.813586e+06\n",
       "1  2019-02-28  2.202022e+06\n",
       "2  2019-03-31  2.807100e+06\n",
       "3  2019-04-30  3.390670e+06\n",
       "4  2019-05-31  3.152607e+06\n",
       "5  2019-06-30  2.577802e+06\n",
       "6  2019-07-31  2.647776e+06\n",
       "7  2019-08-31  2.244468e+06\n",
       "8  2019-09-30  2.097560e+06\n",
       "9  2019-10-31  3.736727e+06\n",
       "10 2019-11-30  3.199603e+06\n",
       "11 2019-12-31  4.613443e+06\n",
       "12 2020-01-31  8.670290e+03"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.DataFrame(y)\n",
    "test.reset_index(inplace=True)\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Date    datetime64[ns]\n",
       "Sales                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "INFO:fbprophet:n_changepoints greater than number of observations. Using 9.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZmPSTLSIiA15DawdbjnQ3Y954qIqq5nYswJjoEL59bsXuVKedmDB/tWmRQUEBUEREBhxjDAcrm7tX7B6q4t0TdXR2GUL9rGR90ozZGcG42FDCA9SMWQYfBUARERkQWju6KDxYSWFxd5uWj+pbAfhKZBB3pMeR7bQxLTGCBFsgvlbN8sngpgAoIiJXrRO1Le7At/VoNW2dhgAfLzLibdw2IZZsp51J8eFEBKlNi8inKQCKiMhVo93Vxbsn6twLOA5VNQMwPDyAG1PCuDY1jhlJESRGBhHgo2bMIheiACgiIv1axemzbCzubtOy5UgNTW0ufLwtpMeG8W/TE8l22Mly2OhsqiU2NravhytyVVAAFBGRfqWzy7CntME9y/f+yUYAhgb7cn1KJDlOO9OS7IyMCiHY7/8+xsqbdYlX5MtSABQRkT5X19LO64erKSyuZNOhamrOtONlgbHRodyV7SDbYSfbaSMmLABvNWMWuWIKgCIi0uuMMfy9osm9gOPPJXV0GQgPsJKVYCfn3IrdMdGhhKlNi0iPUwAUEZFe0dzm4s2jNRSdC30nG88CMDIqmMUZ8WQ77ExNjGC4LQAfNWMW8SgFQBER8Zij1c0UnVvA8faHtbR3GoJ8vckcHs43M+LJcdhJP9emRUR6jwKgiIj0mDZXJ+98WOe+tHu05gwATnsA88fFdD+BIymCpIhA/Kxq0yLSVxQARUTkipxsaKWouIqi4kreOFrDmfZO/Ly9SI8P4+bRw8h22picYCcqWM2YRfoLBUAREbkkrs4udn5UT9GhKgoPVrK/ogmAYSF+zBoZ1d2mJdHOiKhgAn31MSPSH+l/poiIXFRNcxubDldTVFzFpkNV1Ld24G2BcTFh3JPjJNtpY4rDRkxoAF5q0yLS7ykAiojIZxhj2FfWeG4BRxW7Pq7HGLAH+pDjtJPtsDEtKYK0YSGE+qtNi8jVRgFQREQAOH22gzeO1FBYXMnG4ioqmtoAGDU0mH/NHN69gCPRTlxYAFa1aRG5qikAiogMUsYYDlc1U1hcRVFxFdtP1NLRaQj29SYrwca3shKYmmjnmtgwbIFq0yIykFwwAIaEhHzhaq3Tp097ZEAiIuI5rR2dbDtW4760e6KuBYCkiEBuHR/rfs6u0x6Er1WzfCID1QUDYFNT96quH/7whwwbNozbb78dYwwvvvii+3tfpLS0lDvuuINTp07h5eXF0qVLuffeewF46qmnePrpp7Fardx44408+uijAOzfv59vfetbnD59Gi8vL/bs2YO/vz/vvfce3/jGN2htbWX27Nk8+eSTWCwW6urqWLBgASUlJTgcDv7nf/4Hm82GMYZ7772XoqIiAgMD+a//+i8mTJgAQH5+Pj/5yU8A+MEPfsCiRYuurIIiIv3cR3Ut7hW7W4/V0NrRhZ/Vi4z4cBaMjyHbYSMzwUZkkNq0iAwWF70EvHnzZnbt2uX+etmyZWRmZrJixYov3rHVyuOPP86ECRNoamoiPT2d3NxcKisrKSgoYP/+/fj5+VFVVQWAy+Xitttu44UXXmDcuHHU1tbi4+PjPua6deuYPHkys2fPZtOmTcyaNYs1a9Zw3XXXsWrVKtasWcOaNWtYu3YtGzdu5OjRoxw9epRdu3axbNkydu3aRV1dHQ8//DB79+7FYrGQnp7OnDlzsNlsV1JDEZF+paOziz+X1FF4sPvS7oHK7j/aY0P9+edRQ8lx2pmRHEFyZDABPmrGLDIYXTQAent78+KLL5KXl4fFYuG3v/0t3t4X/4URHR1NdHQ00H05OTU1lbKyMp599llWrVqFn58fAFFRUQC8/vrrjB07lnHjxgEQEREBQEVFBadPnyYrKwuAO+64g1dffZVZs2ZRUFDAtm3bAFi0aBEzZsxg7dq1FBQUcMcdd2CxWJg8eTINDQ1UVFSwbds2cnNzsdvtAOTm5rJp0yZuvfXWS6mZiEi/U9nUxqZD3Y9ce/1wNY1nXVi9LFwTG8p3pznJcdjJctgYFuKvNi0icvEA+NJLL3Hvvfdy7733YrFYyM7O5qWXXrqkg5SUlLBv3z4yMzNZvnw527dv54EHHsDf35/HHnuMSZMmceTIESwWCzfccAPV1dXk5eWxYsUKysrKiIuLc+8rLi6OsrIyACorK90hMzo62j2bWFZWRnx8/Gfec6Ht/2jdunWsW7cOgFOnTlFeXn5J53u5qqure+U4A53q2PNU0553pTXtMob9lS28ebyRrSca+eup7nv5IgK8mRIbREZsEFPiQkiMCCTQ99wf7WfqOXXmSkfef+nntGepnp7TH2p70QDocDgoKCi47AM0Nzczb948nnjiCUJDQ3G5XNTX17Nz50727NnD/PnzOX78OC6Xi3fffZc9e/YQGBjIddddR3p6OqGhoZ/Z58XuUTHGfO57LrT9Hy1dupSlS5cCMHHiRGJiYr7s6V6x3jzWQKY69jzVtOddak0bWjt4/XA1RcWVbDxURVVzOxZgTHQI385K6G7T4rQTE+Y/aNu06Oe0Z6mentPXtb1oAKyurubZZ5+lpKQEl8vl3v78889fdOcdHR3MmzePhQsXMnfuXKB71m3u3LlYLBYyMjLw8vKipqaGuLg4pk+fTmRkJACzZ8/m/fff57bbbuPkyZPufZ48edJdtKFDh1JRUUF0dDQVFRXuy8lxcXGUlpZ+5j1xcXHuS8afbJ8xY8ZFz0NEpC8YYzhwquncit1KdpTU09llCPWzkuWwnQt8EYyLDSU8QM2YReTLu2gAvOmmm5g6dSrXX3/9l7r37xPGGJYsWUJqair333+/e/vNN9/M1q1bmTFjBkeOHKG9vZ3IyEhuuOEGHn30UVpaWvD19eXtt9/mvvvuIzo6mpCQEHbu3ElmZia/+c1vuPvuuwGYM2cO+fn5rFq1ivz8fG666Sb39qeffpq8vDx27dpFWFgY0dHR3HDDDXz/+9+nvr4e6L7vcPXq1ZdUMBERT2ppd7H1WC2FByspKq7i44ZWAL4SGcQd6XFkO21MS4wgwRaoNi0ictkuGgBbWlpYu3btJe94x44dvPDCC4wZM4bx48cD8Mgjj7B48WIWL17M6NGj8fX1JT8/H4vFgs1m4/7772fSpElYLBZmz57NjTfeCMAzzzzjbgMza9YsZs2aBcCqVauYP38+69evZ/jw4bz88stA9+xhUVERycnJBAYG8utf/xoAu93Ogw8+yKRJk4DuFjefLAgREekrx2vPdK/YPVTJW8dqaXN1EeDjReZwG7enx5KTaGdiXDgRatMiIj3EYj7vxrhP+cEPfsCUKVOYPXt2b42pX5k4cSJ79+7tlWOVl5f3+T0BA4Hq2PNU057V7uriD3uOsLu6k8KDlRyu7l6ZMTw8gGynjWyHnRlJESRFBuGvNi1fmn5Oe5bq6Tm9WdsL5ZiLzgA++eSTPPLII/j6+uLr64sxBovFoieBiIhcgorTZykqrqKouJItR2poanPh420hPTaMf5ueSPa5Ni1RwX5q0yIiHnfRAPhlnvohIiLn6+wy7P643r2AY19Z9x/NQ4N9uT4lkmsivJh1TSIjo0II9tNj2UWkd130t84nj387ceIEDz74IKWlpVRUVJCRkdEb4xMRuWrUtbSz+VA1hcWVbDpURW1LB14WGBsdyl3ZDrIddrKdNmLCAqg8VUFMjJ5CJCJ946IB8Dvf+Q5eXl5s3bqVBx98kODgYO6880727NnTG+MTEem3jDHsrzh97pFrlfzlo3q6DIQHWJnisJPtsDE9KYLRw0IJU5sWEelHLhoAd+3axfvvv88111wDgM1mo7293eMDExHpj5rbXLxxpJqiQ93P2S1rPAvAyKhgFmfEk+Owk5MYwXBbAD6DtBmziPR/Fw2APj4+dHZ2ulsPVFdX4+WlX2oiMngcqW7uvpfvYCXvHK+lvdMQ5OtN5vBwlmTEk+2wkx7f3aZFRORqcNEAeM8993DLLbdQVVXFAw88wCuvvMJPfvKT3hibiEifONvRyTvHayk8t2r3WE33c3ad9gDmj4vpfgJHUgRJEYH4WdWmRUSuPhcNgAsXLiQ9PZ0333wTYwyvvvoqqampvTE2EZFeU1rfysZD3St23zhSQ0tHJ37eXqTHhzF3TDTZDjuZCTaigtWMWUSufl8YALu6uhg7diwffPABI0eO7K0xiYh4nKuzi50f1btn+fZXdLe8ig7x48bUKLKddqYl2hkRFUygr9q0iMjA8oW/1by8vBg3bhwff/wxw4cP760xiYh4RHVzG5vOLd7YfLia+tYOvL0sjI8J5Z4cJzlOG1kOGzGhAWrGLCID2kX/rK2oqCAtLY2MjAyCgoLc2//4xz96dGAiIleqq8uwr6yRokPdCzh2lzZgDNgDfchxnmvTkhzJqKHBhPqrTYuIDB4XDYDNzc386U9/cn9tjGHlypUeHZSIyOU6fbaDLUeqKTxYxcZDVZxqasMCjBoawr9mDifHaSfHaSc+PACr2rSIyCB10QDocrmYPn36edtaW1s9NiARkUthjOFQVXN3M+ZDlWw/XoeryxDs601Wgo1vZyUwLcnOuJgw7IFq0yIiAl8QAJ955hl+9atfcfz4ccaOHeve3tTURHZ2dq8MTkTk87R2dLLtWM25BRxVnKjrbtOSFBHIwgmxZDvsTE+2k2BTmxYRkc9zwQD49a9/nVmzZvHv//7vrFmzxr09JCQEu93eK4MTEfnER3Ut7hW7W4/V0NrRhb/Vi0nDw1kwPoYch42MBBuRQWrTIiJyMRcMgGFhYYSFhfHb3/62N8cjIgJAR2cXO07UdT+Bo7iSg5XNAMSG+TMnbRg552b5kiODCfDRLJ+IyKVQcysR6Tcqm9rYeC7wvX64mtNtLqxeFibEhvHdaU5yHHayHDaGhfirTYuIyBVQABSRPtPVZdh7ssE9y7e3tBGAyCBfZiZHkOO0My0xgtShIYT469eViEhP0W9UEelVDa0dvH64msLiSjYWV1F9ph0LMCY6hGVTEsh22JnqtBMT5q82LSIiHqIAKCIeZYzhwKkm9wKOHSX1dHYZwvytZCXYyHbameqMYFxsKOEBasYsItIbFABFpMedaXOx9VgNRefatHzc0N079CuRQdyRHke208a0xAgSbIH4WjXLJyLS2xQARaRHHK89427G/NaxWtpcXQT4eJE53Mbt6bHkJNqZGBdOhNq0iIj0OQVAEbks7a4uth+vdT9n93D1GQCG2wKYO2YYOU470xMjSIoMwl9tWkRE+hUFQBH50ipOnz13WbeSLUdqaGpz4eNtIT02jO9NTyTbaWdygo2hIX6a5RMR6ccUAEXkgjq7DLs/rud3e8p4p/Qo+8pOAzA02JfrUyK727Qk2UmNCiHIT79ORESuFvqNLSLnqWtpZ/Oh7jYtmw5VUdvSgZcFxkaHcle2g2yHnWynjZiwALzVjFlE5KqkACgyyBlj2F9xunsBR3Elf/moni4D4QFWshLs5DhtjA3rYuroJMLUpkVEZEBQABQZhJrbXLxxpJqiQ91tWsoazwIwMiqYxRnx5DjsTE2MIN4WgI+3F+Xl5Qp/IiIDiMcacJWWljJz5kxSU1NJS0vjySefdH/vqaeeYsSIEaSlpbFixYrz3vfxxx8THBzMY4895t62adMmRowYQXJyMmvWrHFvP3HiBJmZmaSkpLBgwQLa29sBaGtrY8GCBSQnJ5OZmUlJSYn7PatXryY5OZkRI0awefNmD529SP9ztLqZJ945Tu5//oWIBzdxy3/t5aX3yxgxJIgHc1N4fWkm796VzbPzx7MoYziJkUH46EkcIiIDksdmAK1WK48//jgTJkygqamJ9PR0cnNzqayspKCggP379+Pn50dVVdV577vvvvuYNWuW++vOzk7uvPNOtmzZQlxcHJMmTWLOnDmMGjWKlStXct9995GXl8e3v/1t1q9fz7Jly1i/fj02m41jx46xYcMGVq5cye9+9zsOHjzIhg0bOHDgAOXl5Vx//fUcOXIEb2+1qJCBp83Vydsf1rqfs3uspgUApz2A+eNizi3giCAxIhA/q/4PiIgMJh4LgNHR0URHRwMQEhJCamoqZWVlPPvss6xatQo/Pz8AoqKi3O959dVXSUxMJCgoyL1t9+7dJCcnk5iYCEBeXnpgaNQAABmeSURBVB4FBQWkpqaydetWXnrpJQAWLVrEQw89xLJlyygoKOChhx4C4Gtf+xp33XUXxhgKCgrIy8vDz88Pp9NJcnIyu3fvJisry1NlEOlVpfWtbDzUHfjePFrDmfZO/Ly9SI8PY+6YaLId3W1ahgSrGbOIyGDWK/cAlpSUsG/fPjIzM1m+fDnbt2/ngQcewN/fn8cee4xJkyZx5swZ1q5dy5YtW867/FtWVkZ8fLz767i4OHbt2kVtbS3h4eFYrVb39rKyss+8x2q1EhYWRm1tLWVlZUyePPm8fX3yHpGrkauzi50f1bufs7u/ogmA6BA/Zo+MIttpZ3pSBF8ZEkSgr275FRGRbh7/RGhubmbevHk88cQThIaG4nK5qK+vZ+fOnezZs4f58+dz/PhxfvSjH3HfffcRHBx83vuNMZ/Zp8ViueD2y33Pp61bt45169YBcOrUKcrLy7/cyV6h6urqXjnOQDfQ61jb0sFbJafZeryRbSWnaWzrxNsCo6MC+NdrIpkcG8SEmJBPzfKdoaHmDA1XcMyBXtO+oJr2PNW0Z6mentMfauvRANjR0cG8efNYuHAhc+fOBbpn3ebOnYvFYiEjIwMvLy9qamrYtWsXr7zyCitWrKChoQEvLy/8/f1JT0+ntLTUvc+TJ08SExNDZGQkDQ0NuFwurFare/snxygtLSUuLg6Xy0VjYyN2u929/R/39Y+WLl3K0qVLAZg4ceLnvsZTevNYA9lAqmNXl2FfWaP7kWu7SxswBiICfZiWFEm208b0pEjShoYQ4u+5/9IDqab9hWra81TTnqV6ek5f19ZjnxbGGJYsWUJqair333+/e/vNN9/M1q1bmTFjBkeOHKG9vZ3IyEi2b9/ufs1DDz1EcHAwd911Fy6Xi6NHj3LixAliY2PZsGEDL730EhaLhZkzZ/LKK6+Ql5dHfn4+N910EwBz5swhPz+frKwsXnnlFa699losFgtz5szh61//Ovfffz/l5eUcPXqUjIwMT5VA5LKdPtvBliPVFB6sYuOhKk41tWEBRg0NYWnmcLKddqYm2okLC8CqlboiInKJPBYAd+zYwQsvvMCYMWMYP348AI888giLFy9m8eLFjB49Gl9fX/Lz87/wZnSr1crTTz/NDTfcQGdnJ4sXLyYtLQ2AtWvXkpeXxw9+8AOuueYalixZAsCSJUu4/fbbSU5Oxm63s2HDBgDS0tKYP38+o0aNwmq18stf/lIrgKVfMMZwqKq5uxnzoUq2H6/D1WUI9vUmK8HGt7MSmJZkZ3xMGLZA374eroiIXOUs5vNujBO3iRMnsnfv3l45Vnl5eZ9PCQ8EV0sdWzs62Xas5twCjipO1HW3aUmKCCTHaSfbYWd6sh2HLQhfa9/O8l0tNb2aqKY9TzXtWaqn5/RmbS+UY7QsUKQXfVTX4l6xu/VYDa0dXfhbvZg0PJwF42PIcdjISLARGaQ2LSIi4jkKgCIe1NHZxY4Tde5mzAcrmwGIDfNnTtowcs7N8iVHBhPgo9sRRESkdygAivSwyqY2Np4LfK8fruZ0mwurl4UJsWHcNy2RbIeNLIeNYSH+eHlplk9ERHqfAqDIFerqMuw92eCe5dtb2gjAkCBfrk2JJNthY3pSBKlDQwj20385ERHpe/o0ErkMDa0dvH64msLiSjYWV1F9ph0LMCY6hGVTEshx2Mlx2okND8Bbs3wiItLPKACKfAnGGA6canIv4NhRUk9nlyHM30pWgq27L58zgnGxoYQH+PT1cEVERL6QAqDIBZxpc7H1WA1F59q0fNzQCsBXhgRxR3ocOU470xIjSLAH4KNmzCIichVRABT5lOO1Z9zNmN86Vkubq4sAHy8yh9u4Y2Ic2U4bE+PCiVCbFhERuYopAMqg1u7qYvvxWvdzdg9XnwFguC2AeWOiu5+zmxhBUmQQ/mrTIiIiA4QCoAw65Y1n2Xioe8XulsPVNLd34uNtYWJcON+bPpRsp53JCTaGhvhplk9ERAYkBUAZ8Dq7DLs/rncv4NhXdhqAocG+/NOIIe57+UZGBROkNi0iIjII6NNOBqS6lnY2H+pu07LpUBW1LR14W2BsTCh3ZTvIcdqZ4rARE6Y2LSIiMvgoAMqAYIxhf8VpCg9W8erfSnmv4gxdBmwBPkxx2Mh22JmWZGf0sFDC1KZFREQGOQVAuWo1t7l440g1RYe627SUNZ4F4Ct2P5ZkDCfbYWNaUgRx4WrTIiIi8mkKgHJVOVrdTGFx94rdd47X0t5pCPL1ZnKCjSUZ8eQ4I4iztpCalNDXQxUREem3FAClX2tzdfL2h7Xu5+weq2kBINEeyILxMWQ77ExPisAZEYiftbtNS3l5eV8OWUREpN9TAJR+p7S+1d2m5Y0jNbR0dOLn7cXE+DDmjYlmiqO7TcuQYDVjFhERuRwKgNLnXJ1d7Pzo/9q07K9oAiAm1I8bR0WR7bAzIymClCFBBPrqR1ZERORK6dNU+kR1cxubzi3e2Hy4mvrWDry9LIyPCeXeqU5ynHayEmxEh/rjpTYtIiIiPUoBUHpFV5dhX1mj+5Fru0sbMAYiAn3Icdq7H7mWFMmoocGE+qtNi4iIiCcpAIrHnD7bwZYj1RQVd8/0nWpqwwKkDQthaeZwcpx2chLtxIUFYFWbFhERkV6jACg9xhjDoapm94rd7cfrcHUZQvysTE4IZ9mUBKYm2hkfE4Yt0LevhysiIjJoKQDKFWnt6GTbsZpzCziqOFHX3aYlOTKQ2ybEku3sfgKHwxaEr1WzfCIiIv2BAqBcso/qWtwrdrceq6G1owt/qxcZw8PJGx9DttNOxvBwIoPUpkVERKQ/UgCUi+ro7GLHiTr3pd2Dlc0AxIX5c1PaMHLOzfIlRwYT4OPdx6MVERGRi1EAlM9V2dTGxuIqig5V8vrhahrPurB6WZgQF8b90xLJdtrIctgZGuynNi0iIiJXGQVAAbrbtOw92eCe5dtb2gjAkCBfZiZHkuOwMS0pgtShIQT76cdGRETkaqZP8kGsobWD1w9XU1hcycbiKqrPtONlgdHDQvnOlASyHXZynHZiwwPw1iyfiIjIgKEAOIgYYzhwqsm9gGNHST2dXYYwfytTHDayHXamJkYwNiaU8AA1YxYRERmoPNaXo7S0lJkzZ5KamkpaWhpPPvmk+3tPPfUUI0aMIC0tjRUrVgCwZcsW0tPTGTNmDOnp6WzdutX9+vfee48xY8aQnJzMPffcgzEGgLq6OnJzc0lJSSE3N5f6+nqgO+jcc889JCcnM3bsWN5//333vvLz80lJSSElJYX8/HxPnX6/cabNxWsHTrHslf04fvImYx57m1WFxZxqamPRxDienz+Ovd+dRsHiDB7I/QrTkiIU/kRERAY4j80AWq1WHn/8cSZMmEBTUxPp6enk5uZSWVlJQUEB+/fvx8/Pj6qqKgAiIyN57bXXiImJ4YMPPuCGG26grKwMgGXLlrFu3TomT57M7Nmz2bRpE7NmzWLNmjVcd911rFq1ijVr1rBmzRrWrl3Lxo0bOXr0KEePHmXXrl0sW7aMXbt2UVdXx8MPP8zevXuxWCykp6czZ84cbDabp8rQJz6sOeO+l2/bh7W0uboI9PEmc3g4d0yMI8dpJz0ujMhgv74eqoiIiPQBjwXA6OhooqOjAQgJCSE1NZWysjKeffZZVq1ahZ9fd/iIiooC4JprrnG/Ny0tjbNnz9LW1kZdXR2nT58mKysLgDvuuINXX32VWbNmUVBQwLZt2wBYtGgRM2bMYO3atRQUFHDHHXdgsViYPHkyDQ0NVFRUsG3bNnJzc7Hb7QDk5uayadMmbr31Vk+VoVe0u7rYfrzWfWn3cPUZABJsAcwbE02O08b05EgS7YH4q02LiIjIoNcr9wCWlJSwb98+MjMzWb58Odu3b+eBBx7A39+fxx57jEmTJp33+v/93//lmmuuwc/Pj7KyMuLi4tzfi4uLc88MVlZWukNmdHS0ezaxrKyM+Pj4z7znQtuvRuWNZykqrqToUBVbDlfT3N6Jr7eF9LhwvjdjKDkOO5MddqKC1YxZREREzufxANjc3My8efN44oknCA0NxeVyUV9fz86dO9mzZw/z58/n+PHj7pBy4MABVq5cyeuvvw7gvt/v0y4WaC70ni+7r3Xr1rFu3ToATp06RXl5+cVPtAdUV1df8HudXYZ9p87w5vFGtp5o5IOqVgCGBFqZkRBMZmwQWXEhOOwB55oxGzqbaqlo6pWh9ytfVEe5PKppz1NNe55q2rNUT8/pD7X1aADs6Ohg3rx5LFy4kLlz5wLds25z587FYrGQkZGBl5cXNTU1DBkyhJMnT3LLLbfwm9/8hqSkJPfrT5486d7nyZMniYmJAWDo0KFUVFQQHR1NRUWF+3JyXFwcpaWln3lPXFyc+5LxJ9tnzJjxmXEvXbqUpUuXAjBx4kT38XrDp49Ve6adzYe7n7G76VAVtS0deFtgXEwod2UPJcdpJ9tpIzpUbVr+UW/+mw0WqmnPU017nmras1RPz+nr2nosABpjWLJkCampqdx///3u7TfffDNbt25lxowZHDlyhPb2diIjI2loaODGG29k9erVZGdnu18fHR1NSEgIO3fuJDMzk9/85jfcfffdAMyZM4f8/HxWrVpFfn4+N910k3v7008/TV5eHrt27SIsLIzo6GhuuOEGvv/977tXC7/++uusXr3aUyW4ZMYY/lrW6F7AsfOjeroM2AJ83G1apiXZGT0slDCt1BUREZHL5LEAuGPHDl544QXGjBnD+PHjAXjkkUdYvHgxixcvZvTo0fj6+pKfn4/FYuHpp5/m2LFj/PjHP+bHP/4x0B3QoqKieOaZZ/jGN75Ba2srs2bNYtasWQCsWrWK+fPns379eoYPH87LL78MwOzZsykqKiI5OZnAwEB+/etfA2C323nwwQfd9xz+8Ic/dC8I6SudXYbXDpyisLiK1z6ooPJMBwCjhgazJGM42U470xLtxIUH4OPtsa49IiIiMohYzOfdGCduEydOZO/evR7bf1eXIebhLTS3u0gfFsDMETHkJNqZEBeGPdDXY8cdyMrLy/t8an2gUU17nmra81TTnqV6ek5v1vZCOUZPAuljXl4Wtn0nC19vL7xb60mIj7v4m0RERESugAJgPzByaAgA5eWNfTwSERERGQx0U5mIiIjIIKMAKCIiIjLIKACKiIiIDDIKgCIiIiKDjAKgiIiIyCCjPoAXERkZicPh6JVjVVdXM2TIkF451kCmOvY81bTnqaY9TzXtWaqn5/RmbUtKSqipqfnMdgXAfsTTTacHC9Wx56mmPU817Xmqac9SPT2nP9RWl4BFREREBhkFQBEREZFBxvuhhx56qK8HIf8nPT29r4cwIKiOPU817Xmqac9TTXuW6uk5fV1b3QMoIiIiMsjoErCIiIjIIKMAKCIiIjLIKABegdLSUmbOnElqaippaWk8+eSTANTV1ZGbm0tKSgq5ubnU19cDcOjQIbKysvDz8+Oxxx47b19PPvkko0ePJi0tjSeeeOKCx9y0aRMjRowgOTmZNWvWuLcvXLiQESNGMHr0aBYvXkxHR4cHztgz+lMdP3H33XcTHBzcg2fZu/pTTY0xPPDAA3zlK18hNTWVX/ziFx44Y8/rTzV98803mTBhAuPHjycnJ4djx4554Iw9ry9qunjxYqKiohg9evR52y90zKtJf6rn8uXLGTlyJGPHjuWWW26hoaGhh8+2d11qbV988UXGjh3L2LFjmTJlCn/729/c+7rY588n8vPzSUlJISUlhfz8fABaWlq48cYbGTlyJGlpaaxateryT8rIZSsvLzfvvfeeMcaY06dPm5SUFHPgwAGzfPlys3r1amOMMatXrzYrVqwwxhhTWVlpdu/ebb7//e+bn/3sZ+79/P3vfzdpaWnmzJkzpqOjw1x33XXmyJEjnzmey+UyiYmJ5sMPPzRtbW1m7Nix5sCBA8YYYwoLC01XV5fp6uoyeXl55le/+pWnT7/H9Kc6GmPMnj17zG233WaCgoI8edoe1Z9q+vzzz5vbb7/ddHZ2uo91NepPNU1JSTEHDx40xhjzy1/+0ixatMiTp+4xvV1TY4x5++23zXvvvWfS0tLO236hY15N+lM9N2/ebDo6OowxxqxYseKqrOenXWptd+zYYerq6owxxhQVFZmMjAxjzMU/fz5RW1trnE6nqa2tNXV1dcbpdJq6ujpz5swZs3XrVmOMMW1tbSYnJ8cUFRVd1jlpBvAKREdHM2HCBABCQkJITU2lrKyMgoICFi1aBMCiRYt49dVXAYiKimLSpEn4+Pict5/i4mImT55MYGAgVquV6dOn84c//OEzx9u9ezfJyckkJibi6+tLXl4eBQUFAMyePRuLxYLFYiEjI4OTJ0968tR7VH+qY2dnJ8uXL+fRRx/15Cl7XH+q6TPPPMMPf/hDvLy83Me6GvWnmlosFk6fPg1AY2MjMTExHjtvT+rtmgJMmzYNu93+me0XOubVpD/V85/+6Z+wWq0ATJ48+ar6TPo8l1rbKVOmYLPZgPPP/4v+X3/a5s2byc3NxW63Y7PZyM3NZdOmTQQGBjJz5kwAfH19mTBhwmXXVgGwh5SUlLBv3z4yMzOprKwkOjoa6P6hqaqq+sL3jh49mnfeeYfa2lpaWlooKiqitLT0M68rKysjPj7e/XVcXBxlZWXnvaajo4MXXniBr371qz1wVr2vr+v49NNPM2fOHPdxB4K+rumHH37I7373OyZOnMisWbM4evRoD55d3+jrmj733HPMnj2buLg4XnjhhSu7DNRP9EZNv8ilHrO/6+t6ftrzzz/PrFmzLvv9/c2l1nb9+vXu8/8yn+Nf9nUNDQ289tprXHfddZd1HtbLepecp7m5mXnz5vHEE08QGhp6ye9PTU1l5cqV5ObmEhwczLhx49x/OX2a+ZyOPRaL5byvv/Od7zBt2jSmTp16yePoa31dx/Lycl5++WW2bdt2OcPvl/q6pgBtbW34+/uzd+9efv/737N48WK2b99+6SfTT/SHmv785z+nqKiIzMxMfvazn3H//ffz3HPPXfrJ9BO9VdPBoj/V86c//SlWq5WFCxde1vv7m0ut7VtvvcX69et59913gS/3Of5lXudyubj11lu55557SExMvJRTcNMM4BXq6Ohg3rx5LFy4kLlz5wIwdOhQKioqAKioqPhSl7yWLFnC+++/zzvvvIPdbiclJYXS0lLGjx/P+PHj+c///E/i4uLO+yvs5MmT5136efjhh6muruY//uM/evgsPa8/1HHfvn0cO3aM5ORkHA4HLS0tJCcne+aEe0F/qCl0/+U6b948AG655Rb279/f06faa/pDTaurq/nb3/5GZmYmAAsWLODPf/6zB862d/RmTb/I5RyzP+ov9YTuRQx/+tOfePHFFz835FxtLrW2+/fv51/+5V8oKCggIiIC4IL/r3ft2uWu7R//+MeLft4vXbqUlJQUvvvd717+CV3WnYNijDGmq6vL3H777ebee+89b/v3vve9824KXb58+Xnf/9GPfnTeDbfG/N+N8R999JEZMWKE++bRT+vo6DBOp9McP37cffPoBx98YIwx5tlnnzVZWVmmpaWlx86vt/SnOn7a1bwIpD/VdOXKlWb9+vXGGGPeeustM3HixJ45yV7WX2ra0dFhIiIizOHDh40xxjz33HNm7ty5PXaevam3a/qJEydOfGbRwsWOeTXoT/XcuHGjSU1NNVVVVZd9Pv3Jpdb2o48+MklJSWbHjh3nvf7Lfv7U1tYah8Nh6urqTF1dnXE4HKa2ttYYY8wDDzxg5s6d615Yd7kUAK/A9u3bDWDGjBljxo0bZ8aNG2cKCwtNTU2Nufbaa01ycrK59tpr3f9oFRUVJjY21oSEhJiwsDATGxtrGhsbjTHG5OTkmNTUVDN27FjzxhtvXPCYhYWFJiUlxSQmJpqf/OQn7u3e3t4mMTHRPY6HH37Ysyffg/pTHT/tag6A/amm9fX1Zvbs2Wb06NFm8uTJ5q9//atnT95D+lNNf//735vRo0ebsWPHmunTp5sPP/zQsyfvIX1R07y8PDNs2DBjtVpNbGysee6554wx5oLHvJr0p3omJSWZuLg49zi+9a1veb4AHnSptV2yZIkJDw93vzY9Pd29ry/z+WOMMevXrzdJSUkmKSnJPP/888YYY0pLSw1gRo4c6d73s88+e1nnpEfBiYiIiAwyugdQREREZJBRABQREREZZBQARURERAYZBUARERGRQUYBUERERGSQUQAUEekjDz30EI899lhfD0NEBiEFQBEREZFBRgFQRKQX/fSnP2XEiBFcf/31HD58GIBf/OIXjBo1irFjx5KXl9fHIxSRwWDwPi1bRKSXvffee2zYsIF9+/bhcrmYMGEC6enprFmzhhMnTuDn50dDQ0NfD1NEBgHNAIqI9JLt27dzyy23EBgYSGhoKHPmzAFg7NixLFy4kP/+7//GatXf5SLieQqAIiK9yGKxfGZbYWEhd955J++99x7p6em4XK4+GJmIDCYKgCIivWTatGn84Q9/oLW1laamJl577TW6urooLS1l5syZPProozQ0NNDc3NzXQxWRAU7XGkREesmECRNYsGAB48ePJyEhgalTp2KxWLjttttobGzEGMN9991HeHh4Xw9VRAY4izHG9PUgRERERKT36BKwiIiIyCCjACgiIiIyyCgAioiIiAwyCoAiIiIig4wCoIiIiMggowAoIiIiMsgoAIqIiIgMMv8/1WChtH7KV7IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using facebook prophet:\n",
    "from fbprophet import Prophet\n",
    "df_futur = test[['Order Date', 'Sales']].rename(columns = {'Order Date': 'ds', 'Sales':'y'})\n",
    "df_futur = df_futur.sort_values('ds')\n",
    "df_futur\n",
    "model    = Prophet(seasonality_mode='multiplicative',\n",
    "                interval_width=0.95)\n",
    "model.fit(df_futur)\n",
    "future   = model.make_future_dataframe(periods=2)\n",
    "forecast = model.predict(future)\n",
    "figure   = model.plot(forecast, xlabel='Date', ylabel='Sales')\n",
    "figure2  = model.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>2.617446e+06</td>\n",
       "      <td>509434.207195</td>\n",
       "      <td>4.639538e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>2.623425e+06</td>\n",
       "      <td>665200.976225</td>\n",
       "      <td>4.613117e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-31</td>\n",
       "      <td>2.630045e+06</td>\n",
       "      <td>565368.637161</td>\n",
       "      <td>4.649313e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>2.636451e+06</td>\n",
       "      <td>574097.392826</td>\n",
       "      <td>4.623833e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>2.643071e+06</td>\n",
       "      <td>666400.353369</td>\n",
       "      <td>4.732366e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>2.649478e+06</td>\n",
       "      <td>625711.363281</td>\n",
       "      <td>4.670055e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2.656098e+06</td>\n",
       "      <td>649190.772214</td>\n",
       "      <td>4.659502e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2.662718e+06</td>\n",
       "      <td>609348.041379</td>\n",
       "      <td>4.662290e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.669124e+06</td>\n",
       "      <td>589958.279581</td>\n",
       "      <td>4.703295e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>2.675744e+06</td>\n",
       "      <td>594934.524177</td>\n",
       "      <td>4.673692e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-11-30</td>\n",
       "      <td>2.682150e+06</td>\n",
       "      <td>481226.880056</td>\n",
       "      <td>4.626522e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2.688770e+06</td>\n",
       "      <td>537475.609122</td>\n",
       "      <td>4.651659e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2.695390e+06</td>\n",
       "      <td>677927.543686</td>\n",
       "      <td>4.646084e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2.695604e+06</td>\n",
       "      <td>580161.784143</td>\n",
       "      <td>4.869637e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2.695817e+06</td>\n",
       "      <td>712877.850264</td>\n",
       "      <td>4.699507e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds          yhat     yhat_lower    yhat_upper\n",
       "0  2019-01-31  2.617446e+06  509434.207195  4.639538e+06\n",
       "1  2019-02-28  2.623425e+06  665200.976225  4.613117e+06\n",
       "2  2019-03-31  2.630045e+06  565368.637161  4.649313e+06\n",
       "3  2019-04-30  2.636451e+06  574097.392826  4.623833e+06\n",
       "4  2019-05-31  2.643071e+06  666400.353369  4.732366e+06\n",
       "5  2019-06-30  2.649478e+06  625711.363281  4.670055e+06\n",
       "6  2019-07-31  2.656098e+06  649190.772214  4.659502e+06\n",
       "7  2019-08-31  2.662718e+06  609348.041379  4.662290e+06\n",
       "8  2019-09-30  2.669124e+06  589958.279581  4.703295e+06\n",
       "9  2019-10-31  2.675744e+06  594934.524177  4.673692e+06\n",
       "10 2019-11-30  2.682150e+06  481226.880056  4.626522e+06\n",
       "11 2019-12-31  2.688770e+06  537475.609122  4.651659e+06\n",
       "12 2020-01-31  2.695390e+06  677927.543686  4.646084e+06\n",
       "13 2020-02-01  2.695604e+06  580161.784143  4.869637e+06\n",
       "14 2020-02-02  2.695817e+06  712877.850264  4.699507e+06"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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